Pub Date : 2023-04-25DOI: 10.1080/03081079.2023.2201001
S. E. I. Bouzeraa, R. Bououden, Mohammed Salah Abdelouahab
ABSTRACT This paper investigates the discrete fractional-order logistic map and reports its fractional order with a fixed memory length version. The dynamic of the proposed system is analyzed using the bifurcation diagram and the recently introduced 0–1 test. It is shown that this fractional order with a fixed memory length logistic map can exhibit rich nonlinear dynamics such as period-doubling bifurcation and chaos. Furthermore, it does not require significant time to perform the calculations.
{"title":"Fractional logistic map with fixed memory length","authors":"S. E. I. Bouzeraa, R. Bououden, Mohammed Salah Abdelouahab","doi":"10.1080/03081079.2023.2201001","DOIUrl":"https://doi.org/10.1080/03081079.2023.2201001","url":null,"abstract":"ABSTRACT This paper investigates the discrete fractional-order logistic map and reports its fractional order with a fixed memory length version. The dynamic of the proposed system is analyzed using the bifurcation diagram and the recently introduced 0–1 test. It is shown that this fractional order with a fixed memory length logistic map can exhibit rich nonlinear dynamics such as period-doubling bifurcation and chaos. Furthermore, it does not require significant time to perform the calculations.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"653 - 663"},"PeriodicalIF":2.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47781082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-25DOI: 10.1080/03081079.2023.2201901
Yan Sun, B. Pang, Jusheng Mi
ABSTRACT Recently, Jiang, H. B., and B. Q. Hu. [2022. “On (O,G)-Fuzzy Rough Sets Based on Overlap and Grouping Functions Over Complete Lattices.” International Journal of Approximate Reasoning 144: 18–50. doi:10.1016/j.ijar.2022.01.012] constructed a -fuzzy rough set model with the logical connectives–a grouping function and an overlap function on a complete lattice, which provided a new constructive approach to fuzzy rough set theory. The axiomatic approach is as important as the constructive approach in rough set theory. In this paper, we continue to study axiomatic characterizations of -fuzzy rough set. Traditionally, the associativity of the logical connectives plays a vital role in the axiomatic research of existing fuzzy rough set models. However, a grouping function and an overlap function lack the associativity. So we explore a novel axiomatic approach to -upper and -lower fuzzy rough approximation operators without associativity. Further, we provide single axioms to characterize -upper and -lower fuzzy rough approximation operators instead of sets of axioms. Finally, we use single axioms to characterize fuzzy rough approximation operators generated by various kinds of fuzzy relations including serial, reflexive, symmetric, -transitive, -transitive fuzzy relations as well as their compositions.
{"title":"Axiomatic characterizations of (𝔾, 𝕆)-fuzzy rough approximation operators via overlap and grouping functions on a complete lattice","authors":"Yan Sun, B. Pang, Jusheng Mi","doi":"10.1080/03081079.2023.2201901","DOIUrl":"https://doi.org/10.1080/03081079.2023.2201901","url":null,"abstract":"ABSTRACT Recently, Jiang, H. B., and B. Q. Hu. [2022. “On (O,G)-Fuzzy Rough Sets Based on Overlap and Grouping Functions Over Complete Lattices.” International Journal of Approximate Reasoning 144: 18–50. doi:10.1016/j.ijar.2022.01.012] constructed a -fuzzy rough set model with the logical connectives–a grouping function and an overlap function on a complete lattice, which provided a new constructive approach to fuzzy rough set theory. The axiomatic approach is as important as the constructive approach in rough set theory. In this paper, we continue to study axiomatic characterizations of -fuzzy rough set. Traditionally, the associativity of the logical connectives plays a vital role in the axiomatic research of existing fuzzy rough set models. However, a grouping function and an overlap function lack the associativity. So we explore a novel axiomatic approach to -upper and -lower fuzzy rough approximation operators without associativity. Further, we provide single axioms to characterize -upper and -lower fuzzy rough approximation operators instead of sets of axioms. Finally, we use single axioms to characterize fuzzy rough approximation operators generated by various kinds of fuzzy relations including serial, reflexive, symmetric, -transitive, -transitive fuzzy relations as well as their compositions.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"664 - 693"},"PeriodicalIF":2.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44292783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-16DOI: 10.1080/03081079.2023.2196620
Zhaowen Li, Yiying Chen
A discrete feature space consists of the set of samples, the set of categorical attributes (features) describing the samples and a decision attribute. Generally, many feature selection algorithms for a discrete feature space are based on the lower approximation or information entropy. However, the calculation of the lower approximation is more cumbersome, and information entropy may result based on equivalence class in a poor selection of features. This paper proposes feature selection algorithm for a discrete feature space by using fuzzy conditional information entropy iterative strategy and matrix operation. Firstly, the fuzzy symmetry relation induced by a discrete feature space is defined. Then, fuzzy conditional and joint information entropy for a discrete feature space are presented, and some properties are obtained. Subsequently, fuzzy conditional information entropy iterative model (FCIEI-model) is proposed. Moreover, difference, block diagonal, and decision block diagonal matrices are introduced. Next, a feature selection algorithm (denoted as FDM-algorithm) on account of FCIEI-model and matrix operations is designed and its time complexity is analyzed. Finally, the performance of the algorithm is evaluated through a series of experiments. The results show that the given algorithm is better than the existing algorithms.
{"title":"Feature selection in a discrete feature space based on fuzzy conditional information entropy iterative model and matrix operation","authors":"Zhaowen Li, Yiying Chen","doi":"10.1080/03081079.2023.2196620","DOIUrl":"https://doi.org/10.1080/03081079.2023.2196620","url":null,"abstract":"A discrete feature space consists of the set of samples, the set of categorical attributes (features) describing the samples and a decision attribute. Generally, many feature selection algorithms for a discrete feature space are based on the lower approximation or information entropy. However, the calculation of the lower approximation is more cumbersome, and information entropy may result based on equivalence class in a poor selection of features. This paper proposes feature selection algorithm for a discrete feature space by using fuzzy conditional information entropy iterative strategy and matrix operation. Firstly, the fuzzy symmetry relation induced by a discrete feature space is defined. Then, fuzzy conditional and joint information entropy for a discrete feature space are presented, and some properties are obtained. Subsequently, fuzzy conditional information entropy iterative model (FCIEI-model) is proposed. Moreover, difference, block diagonal, and decision block diagonal matrices are introduced. Next, a feature selection algorithm (denoted as FDM-algorithm) on account of FCIEI-model and matrix operations is designed and its time complexity is analyzed. Finally, the performance of the algorithm is evaluated through a series of experiments. The results show that the given algorithm is better than the existing algorithms.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"597 - 635"},"PeriodicalIF":2.0,"publicationDate":"2023-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42239079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-04DOI: 10.1080/03081079.2023.2196421
G. Çayli
In this article, we first introduce a method for obtaining uninorms on a bounded lattice with the identity element using an interior operator on and a t-conorm on Next, based on a closure operator on and a t-norm on a construction method for uninorms on with the identity element is proposed. Furthermore, we state that these methods are generalizations of those described in [Çaylı, G. D. 2018. “A Characterization of Uninorms on Bounded Lattices by Means of Triangular Norms and Triangular Conorms.” International Journal of General Systems 47: 772–793. doi:10.1080/03081079.2018.1513929; Dan, Y., and B. Q. Hu. 2020. “A New Structure for Uninorms on Bounded Lattices.” Fuzzy Sets and Systems 386: 77–94. doi:10.1016/j.fss.2019.02.001.]. Some corresponding examples are also provided to illustrate that our methods differ from the existing ones.
{"title":"An alternative construction of uninorms on bounded lattices","authors":"G. Çayli","doi":"10.1080/03081079.2023.2196421","DOIUrl":"https://doi.org/10.1080/03081079.2023.2196421","url":null,"abstract":"In this article, we first introduce a method for obtaining uninorms on a bounded lattice with the identity element using an interior operator on and a t-conorm on Next, based on a closure operator on and a t-norm on a construction method for uninorms on with the identity element is proposed. Furthermore, we state that these methods are generalizations of those described in [Çaylı, G. D. 2018. “A Characterization of Uninorms on Bounded Lattices by Means of Triangular Norms and Triangular Conorms.” International Journal of General Systems 47: 772–793. doi:10.1080/03081079.2018.1513929; Dan, Y., and B. Q. Hu. 2020. “A New Structure for Uninorms on Bounded Lattices.” Fuzzy Sets and Systems 386: 77–94. doi:10.1016/j.fss.2019.02.001.]. Some corresponding examples are also provided to illustrate that our methods differ from the existing ones.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"574 - 596"},"PeriodicalIF":2.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46193564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/03081079.2023.2194642
Somya Jain, Adwitiya Sinha
The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.
{"title":"TriBeC: identifying influential users on social networks with upstream and downstream network centrality","authors":"Somya Jain, Adwitiya Sinha","doi":"10.1080/03081079.2023.2194642","DOIUrl":"https://doi.org/10.1080/03081079.2023.2194642","url":null,"abstract":"The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"275 - 296"},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47218573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/03081079.2023.2195173
N. Charbel, C. Sallaberry, Sébastien Laborie, R. Chbeir
ABSTRACT Adopting semantic technologies has proven several benefits for enabling a better representation of the data and empowering reasoning capabilities over it. However, there are still unresolved issues, such as the shift from heterogeneous documents to semantic data models and the representation of search results. Thus, in this paper, we introduce a novel F ram E work for hybrid mol E cule-base D SE mantic SEARCH , entitled FEED2SEARCH, which facilitates Information Retrieval over a heterogeneous document corpus. We first propose a semantic representation of the corpus, which automatically generates a semantic graph covering both structural and domain-specific aspects. Then, we propose a query processing pipeline based on a novel data structure for query answers, extracted from this graph, which embeds core information together with structural-based and domain-specific context. This provides users with interpretable search results, helping them understand relevant information and track cross document dependencies. A set of experiments conducted using real-world construction projects from the Architecture, Engineering and Construction (AEC) industry shows promising results, which motivates us to further investigate the effectiveness of our proposal in other domains.
{"title":"FEED2SEARCH: a framework for hybrid-molecule based semantic search","authors":"N. Charbel, C. Sallaberry, Sébastien Laborie, R. Chbeir","doi":"10.1080/03081079.2023.2195173","DOIUrl":"https://doi.org/10.1080/03081079.2023.2195173","url":null,"abstract":"ABSTRACT Adopting semantic technologies has proven several benefits for enabling a better representation of the data and empowering reasoning capabilities over it. However, there are still unresolved issues, such as the shift from heterogeneous documents to semantic data models and the representation of search results. Thus, in this paper, we introduce a novel F ram E work for hybrid mol E cule-base D SE mantic SEARCH , entitled FEED2SEARCH, which facilitates Information Retrieval over a heterogeneous document corpus. We first propose a semantic representation of the corpus, which automatically generates a semantic graph covering both structural and domain-specific aspects. Then, we propose a query processing pipeline based on a novel data structure for query answers, extracted from this graph, which embeds core information together with structural-based and domain-specific context. This provides users with interpretable search results, helping them understand relevant information and track cross document dependencies. A set of experiments conducted using real-world construction projects from the Architecture, Engineering and Construction (AEC) industry shows promising results, which motivates us to further investigate the effectiveness of our proposal in other domains.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"343 - 383"},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45813251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/03081079.2023.2200248
L. Berkani, Nassim Boudjenah
Deep learning has yielded success in many research fields. In the last few years, deep learning techniques have been applied in recommender systems to solve cold start and data sparsity problems. However, only a few attempts have been made in social-based recommender systems. In this study, we address this issue and propose a novel recommendation model called Sentiment based Social Neural Hybrid Filtering (S-SNHF). This model combines collaborative and content-based filtering with social information using a deep neural architecture based on Generalized Matrix Factorization (GMF) and Hybrid Multilayer Perceptron (HybMLP). Furthermore, for achieving higher recommendation reliability, the hybrid sentiment analysis model is integrated to analyse users’ opinions and infer their preferences. The results of the empirical study performed with three popular datasets show the contribution of both, social information and sentiment analysis on the recommendation performance and that our approach achieves significantly better recommendation accuracy, compared with state-of-the-art recommendation methods.
{"title":"S-SNHF: sentiment based social neural hybrid filtering","authors":"L. Berkani, Nassim Boudjenah","doi":"10.1080/03081079.2023.2200248","DOIUrl":"https://doi.org/10.1080/03081079.2023.2200248","url":null,"abstract":"Deep learning has yielded success in many research fields. In the last few years, deep learning techniques have been applied in recommender systems to solve cold start and data sparsity problems. However, only a few attempts have been made in social-based recommender systems. In this study, we address this issue and propose a novel recommendation model called Sentiment based Social Neural Hybrid Filtering (S-SNHF). This model combines collaborative and content-based filtering with social information using a deep neural architecture based on Generalized Matrix Factorization (GMF) and Hybrid Multilayer Perceptron (HybMLP). Furthermore, for achieving higher recommendation reliability, the hybrid sentiment analysis model is integrated to analyse users’ opinions and infer their preferences. The results of the empirical study performed with three popular datasets show the contribution of both, social information and sentiment analysis on the recommendation performance and that our approach achieves significantly better recommendation accuracy, compared with state-of-the-art recommendation methods.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"297 - 325"},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49178551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19.
{"title":"A hybrid approach based on linguistic analysis and fuzzy logic to ensure the surveillance of people having paranoid personality disorder towards Covid-19 on social media","authors":"Mourad Ellouze, Seifeddine Mechti, Lamia Hadrich Belguith","doi":"10.1080/03081079.2023.2195174","DOIUrl":"https://doi.org/10.1080/03081079.2023.2195174","url":null,"abstract":"This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"251 - 274"},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48538925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/03081079.2023.2204251
Sadok Ben Yahia, C. Attiogbe
Disruptive changes in the industrial environment have occurred in recent years owing to rapid advancements in electronics, information, and communication technology. Because of the ever-increasing demands for product quality and economic benefit, intelligent components and devices are implemented, and networked and real-time supervision and control systems are also running in parallel. As a consequence, the level of automation in modern industrial systems is steadily rocketing. In addition, the increased availability of different data types paves the way to stunning scenarios for applying data-driven modeling techniques. The latter are revolutionizing complex systems’ modeling, prediction, and control. Fresh advances in scientific computing witness how data-driven methods can be applied to diverse, complex systems. Applications of Artificial intelligence-based systems play a pivotal role at the crossroads of almost all fields of computer science. Recent advances in big data generation and management have allowed decision-makers to utilize these overwhelming volumes of data for various purposes and analyses. This special issue consists of selected papers from an open call as well as thoroughly revised papers from the 2021 International Conference on Model and Data Engineering (MEDI’2021) held remotely in Tallinn (Estonia) (Attiogbe and Ben Yahia 2021). This special issue unveils new trends in developing data-driven application systems that seek to adapt computational algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modeling. Original research and review work with models and building data-driven applications using computational algorithms were particularly sought after. This special issue, aiming to provide state-of-the-art information to academics, researchers, and industry practitioners on Advances in Data-driven Engineering, attracted a total of eleven (11) submissions, five (5) of which had their initial versions among the sixteen (16) full papers presented during the MEDI’2021 conference. The remaining articles are contributions submitted in response to the general call for the special issue. Among the eleven submitted papers, the following six (6) papers were accepted after a thorough two-level reviewing process. The first paper in this special issue is authored by Garcia-Garcia et al. (2023). The authors introduced the design and the implementation of efficient distributed algorithms for distance join queries in Spark-based spatial analytics systems. They look into how to make and use efficient distance-based join queries and distributed algorithms in Apache Sedona. The authors improved the new in-memory cluster computing system for processing large-scale spatial data using the best spatial partitioning and other optimization techniques. In the second paper, Ellouze,Mechtib, and Belguith (2023) proposed a supervised learning method leveraging multim
{"title":"Preface of the special issue on advances in data-driven engineering","authors":"Sadok Ben Yahia, C. Attiogbe","doi":"10.1080/03081079.2023.2204251","DOIUrl":"https://doi.org/10.1080/03081079.2023.2204251","url":null,"abstract":"Disruptive changes in the industrial environment have occurred in recent years owing to rapid advancements in electronics, information, and communication technology. Because of the ever-increasing demands for product quality and economic benefit, intelligent components and devices are implemented, and networked and real-time supervision and control systems are also running in parallel. As a consequence, the level of automation in modern industrial systems is steadily rocketing. In addition, the increased availability of different data types paves the way to stunning scenarios for applying data-driven modeling techniques. The latter are revolutionizing complex systems’ modeling, prediction, and control. Fresh advances in scientific computing witness how data-driven methods can be applied to diverse, complex systems. Applications of Artificial intelligence-based systems play a pivotal role at the crossroads of almost all fields of computer science. Recent advances in big data generation and management have allowed decision-makers to utilize these overwhelming volumes of data for various purposes and analyses. This special issue consists of selected papers from an open call as well as thoroughly revised papers from the 2021 International Conference on Model and Data Engineering (MEDI’2021) held remotely in Tallinn (Estonia) (Attiogbe and Ben Yahia 2021). This special issue unveils new trends in developing data-driven application systems that seek to adapt computational algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modeling. Original research and review work with models and building data-driven applications using computational algorithms were particularly sought after. This special issue, aiming to provide state-of-the-art information to academics, researchers, and industry practitioners on Advances in Data-driven Engineering, attracted a total of eleven (11) submissions, five (5) of which had their initial versions among the sixteen (16) full papers presented during the MEDI’2021 conference. The remaining articles are contributions submitted in response to the general call for the special issue. Among the eleven submitted papers, the following six (6) papers were accepted after a thorough two-level reviewing process. The first paper in this special issue is authored by Garcia-Garcia et al. (2023). The authors introduced the design and the implementation of efficient distributed algorithms for distance join queries in Spark-based spatial analytics systems. They look into how to make and use efficient distance-based join queries and distributed algorithms in Apache Sedona. The authors improved the new in-memory cluster computing system for processing large-scale spatial data using the best spatial partitioning and other optimization techniques. In the second paper, Ellouze,Mechtib, and Belguith (2023) proposed a supervised learning method leveraging multim","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"203 - 205"},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48626017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1080/03081079.2023.2184812
D. Jocic, I. Štajner-Papuga
This paper deals with both conditional and regular distributivity between the special class of uni-nullnorms and general uninorms. The presented research is a natural continuation and extension of some previous works with a shift in focus from uninorms from the class to a much wider class of uninorms.
{"title":"Distributivity and conditional distributivity of uni-nullnorms over uninorms","authors":"D. Jocic, I. Štajner-Papuga","doi":"10.1080/03081079.2023.2184812","DOIUrl":"https://doi.org/10.1080/03081079.2023.2184812","url":null,"abstract":"This paper deals with both conditional and regular distributivity between the special class of uni-nullnorms and general uninorms. The presented research is a natural continuation and extension of some previous works with a shift in focus from uninorms from the class to a much wider class of uninorms.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"546 - 573"},"PeriodicalIF":2.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41403402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：